ABSTRACT Cachexia, featuring rapid loss of weight and muscle, is common to many complex diseases such as chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), cancer and AIDS. Regardless of the primary disease diagnosis, the presence of cachexia is associated with poor prognosis. Equally important is the observation that not every patient diagnosed with a complex disease such as COPD and CHF becomes cachectic. This information motivated me to hypothesize that there are common genes and pathways influencing cachexia in these different complex, chronic traits. For a first step in my long-term career plan to develop a research program on network medicine approaches to cachexia in complex disease, I plan to primarily study cachexia in COPD patient populations. COPD is the third leading cause of death in the United States and it has been estimated that as high as 20% of COPD cases develop cachexia, however this number may be overestimated due to limitations associated with defining cachexia. The development of cachexia is a strong predictor of mortality. The public health impact of elucidating determinants of COPD cachexia coincides with my training in the Channing Division of Network Medicine where I have access to several well- characterized COPD populations (NH5,300 COPD cases with longitudinal measures). We propose the first application of a multi-stage GWAS to cachexia in any disease. Research on cachexia in COPD will inform cachexia research for other chronic diseases. Network medicine applies systems biology approaches, such as integration of data from genotypes, gene expression levels and protein-protein interactions, to try to understand how perturbations in the system may lead to complex diseases. This study has three specific aims. (1) We will investigate the genetics of COPD cachexia in three well-characterized COPD populations (ECLIPSE, TESRA and COPDGene) and we will investigate the association of whole-exome sequencing (WES) variants with cachexia in COPD cases from COPDGene. We will integrate findings from common genetic variants with protein-protein interactions to identify novel and/or known disease modules. (2) We will recruit a new COPD case population in order to demonstrate that fat-free mass (FFM) measure using Dual X- ray Absorptiometry (DXA) correlates with pectoralis muscle area (PMA) measured from chest computed tomography (CT) scans. Further, we will test for correlation between gene expression signatures with PMA and FFM. (3) We will search for gene expression signatures that are associated with cachexia and generate a differential co-expression network. These findings will improve our understanding of the etiology and epidemiology of cachexia in COPD. This will be the first step in a long term plan to probe the cachexia network medicine landscape of complex diseases including COPD, CHF, cancer and AIDS.